Abstract: Method and device(s) (400) for matching of object model instances (105a-c) with imaged object instances (103a-c) in an image (101). Said object model instances (105a-c) being instances of an object model (105) of an object. Said matching starts from the object model instances (105a-c) having hypothetical transforms, respectively, in the image (101) for matching with the imaged object instances (103a-c), and wherein said matching is based on transforming respective object model instance (105a;b;c) to more accurately match with respective imaged object instance (103a;b;c) in the image (101). It is obtained (301) predetermined information regarding how the imaged object instances (103a-c) relate to each other in the image (101) in addition to what said object model (105) as such discloses about respective imaged object instance (103a;b;c) in the image (101). Said matching is then performed (302) using said obtained predetermined information.

Abstract: Device(s) and method supporting generation of a second object model, based on a first object model, for object matching according to an object matching algorithm. The first object model comprising object features of an imaged reference object. It is obtained sub-models that comprise different sub-features, respectively, of said object features comprised in the first object model. It is provided contribution indicators for the sub-models, respectively. Each contribution indicator indicating contribution of the sub-feature to incorrect matches. The contribution indicators being based on matching, according to the object matching algorithm, the first object model and the sub-models with at least one model optimization image comprising predefined training features that when matched with the first object model result in at least said incorrect matches.

Abstract: Device(s) and method supporting generation of a second object model, based on a first object model, for object matching according to an object matching algorithm. The first object model comprising object features of an imaged reference object. It is obtained sub-models that comprise different sub-features, respectively, of said object features comprised in the first object model. It is provided contribution indicators for the sub-models, respectively. Each contribution indicator indicating contribution of the sub-feature to incorrect matches. The contribution indicators being based on matching, according to the object matching algorithm, the first object model and the sub-models with at least one model optimization image comprising predefined training features that when matched with the first object model result in at least said incorrect matches.